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Friday, December 21, 2012

Ever wonder how the quality of your drinking water is ensured? A combination of good water sources, regulations that set the standards, water treatment infrastructure, and a testing laboratory. But it takes people to operate the infrastructure and chemists, microbiologists, and technicians to test the water quality. Who are these people?

Linnea Hoover, A former colleague who supervises a chemistry section at the East Bay Municipal Utility District's Laboratory, has combined her interests in science, photography, and art to show the human side of the laboratory. In a gallery of photographs that display the many faceted personalities in the laboratory, Linnea has matched her staff with icons of art and history in a creative collection you can view at:

While the photographic display shows that humor and science can coexist, it is important to remember that the people you saw in the gallery take their jobs seriously and understand the need to maintain and calibrate their instrumentation; perform tests to ensure the results are reproducible and correct; and that when the numbers say a substance is not analytically present its analytical absence is confirmed. When coworkers understand their profession, work as a team, and can even express joy in their work the product will show it.

Wednesday, December 12, 2012

I recently joined a photography Meetup that convenes every two weeks to critique a photography challenge (http://www.meetup.com/Tinkering-Toms-and-Tammys-Photography-Group/). The challenge for our second meetup was to capture a progression or stages of time, like the metamorphosis of a butterfly, frying an egg, or changes of the seasons.I decided to assemble a progression in time with two photos I already had and were separated by several decades. In fact I was not the photographer. A few years ago I assembled a rather large collection of family photographs dating to 1889. Some were still in excellent condition, but many showed the toll of time. Months of work were required to restore many badly faded, mildewed, and otherwise noticeably aged prints. There were two photographs that stood out: a great aunt-in-law, Anna Elizabeth Bell Graham, as a young woman and again in her late years showing a remarkable conformity. While she had obviously aged, the shape and structure of her face was relatively unchanged and her presentation to the camera was the same in both photographs. I had some prior experience merging photographs of relatives to show the similarities and differences to others in the family, but had not previously seen such a judicious combination. So I decided these photographs of Anna Bell would be assembled into a progression of time for the Meetup photography challenge. Anna Bell was born Jan 30, 1884 Anna Elizabeth Bell in West Shokan, NY. The photograph below shows her at the age of five with her younger brother Charles. Fortunately it was the practice of past generations to write information, in pencil, on the back of family photos. That practice is seldom practiced today.

When Anna's family moved to Illinois, she grew up in a rural setting surrounded by farmland.

A photograph of Anna as a young woman in her late teens.

A photograph of Anna in her late years.

Using these two photographs and Photoshop, I created a sequence of five images showing how she might have looked over the intervening years.

Mouse Click for Larger View

I did not know Anna Bell, but I leave one other photograph that has no information on the back and none of my in-laws know exactly when the photograph was made, but it is visibly neither when she was in her youth nor old age. You can judge for yourself whether the sequence above has recaptured Anna Bell in the intervening years.

Friday, November 30, 2012

Light meter?Isn’t that something like a sliderule?A few photographers still use light meters
but most of us depend on the histogram to tell us if an image is over or under
exposed.And some know about histograms
but don’t use them because they rely on the camera to make all of the
photographic decisions.Nothing wrong
with that if you have no desire or need for creative control, but if you’d like
to go the next step and not let the camera make all the decisions, then this
little tutorial is for you.

A histogram plots light received
by the camera’s sensor with light value the horizontal axis and number of
pixels on the vertical axis as in the plot below.

Light
values for 8 bit pixels range from 0 (no light) to 255 (blown out highlight).The next chart shows light value assignments
and the relationship to the Adams Zone System numbers.This makes for a total of 256X256X256 color
shades when the Red, Green, and Blue (RGB) channels are combined for full color.Our eyes can see more shades of color than
that but it’s close enough and many color printers can’t do any better.

So
how do you use a histogram for proper exposure?

Below
is a properly exposed photo shot at sunset.There are no blown out highlights or blocked up shadows.

Abandoned
Building at Sunset

Now compare to the badly
overexposed image below.

Here is the same photo with
the histogram superimposed.

Note that the histogram is
pushed to the right with no space on the right side: the highlights are
blown.

An underexposed version
of the same scene.

Perhaps
you want that nighttime look.That’s
good and exactly why you want control over your shot.Before you look at the histogram below, what
will the curve look like?

Of course you knew the curve
would be pushed to the left with the shadows blocked, yes?

So what does a ‘proper’
histogram look like?If you don’t want
blocked shadows or blown highlights, then you will want a histogram that has
nothing extending to the edges, as in the next image.

So that’s it. Properly
exposed, a photograph should have neither blocked shadows nor blown
highlights.Shadow areas that are too
dark have no detail and generally lack interest.Highlights that are blown also have no detail
and are usually distracting to the central features of the image.

And now you know what happens
to the histogram if the shadows are too dark or the highlights are too
bright.Of course that is not to say
that the artist’s intention was not have shadows without detail or highlights
that dazzled the eyes.After all,
photography is an art.

Sunday, November 11, 2012

Remember Hi8 video? If you are like me you will have several 20+ year old pre-digital analogue video recordings you may wonder what to do with. You could just forget about them for several more years and let someone else worry about what to do with them or you could convert them to a digital format, put them on a hard drive, and even edit them for the memorable segments like your daughter's first steps, your visit to Paris in 1990, or the historic footage you shot and is now no longer possible because the old factory has been replaced with condominiums.

Having 30 Hi8 tapes representing over 50 hours of video, I decided it was time to recover some of the past and to perform the remastering from analogue to digital myself. My setup for the process includes an iMac computer, a Sony TR700 video camera (the one originally used to shoot the Hi8 videos), a Elgato Video Capture converter, and cabling to connect camera to Elgato. The Elgato takes both S-Video and Composite Video inputs and provides a USB output. It records in the H.264 mp4 format, which maxes out at 640x480. Seems low rez by today's standards, but Hi8 is not today's standard. Elgato is simple to use but lacks on-the-fly controls although you can batch control brightness, contrast, etc through the Preferences settings. More info on Elgato at http://www.elgato.com/elgato/na/mainmenu/products/Video-Capture/product1.en.html.

So far I've remastered some 20 hours of video and am mostly satisfied with the quality. Some actually looks a bit nicer than the original when played directly from the camcorder to TV monitor. Some of the converted footage exhibits 'jitters' which are generally not overly noticeable.

Once a tape has been converted to digital video (DV), it's possible to edit selections. I used iMovie for that purpose. I selected a 30 min segment from a 2 hour tape then edited it to a 7 min video, The Scotia Sawmill - 1993. Scotia was a company town dominated by the Pacific Lumber Company (PALCO). See http://en.wikipedia.org/wiki/Pacific_Lumber_Company for more information.

You may need to convert the video format and/or compress it for posting on your blog (Blogger limits file size to 100MB). MPEG Streamclip, free software that will convert formats and compress video files is available for both Mac in PC at http://www.squared5.com/svideo/mpeg-streamclip-mac.html.

Tuesday, July 03, 2012

So you've been storing all your photos using iPhoto and you didn't make a backup. Not a good idea, but some folks still do that and even if you haven't and iPhoto says it's broken how do you get to your photos?

This happened to a friend and while some of her photos were backed up, there were problems with the backup. Problems with backups are not always apparent until you decide you need to restore some files then find out what you thought was backed up isn't. If this happens because something else went snapfoo your options rapidly dwindle.

So how do you access images stored in the iPhoto application when the application malfunctions? iPhoto stores the images in folders under the Pictures master folder. The iPhoto application resides within the Pictures folder and acts to retrieve images in the Pictures subfolders. However, it is possible to locate the hidden subfolders. Let me explain how I did this on my MAC. A similar process should work on a PC.

Even if you store your photos using iPhoto, you probably have one or more images stored elsewhere on one of your hard drives, maybe even the computer's internal hard drive. Enter the file name in the Finder search window and it will display the folders path of the file for each instance of the image. You may have may but probably only one in the Pictures folder. If several, check each one until you find a file path that includes iPhoto Library. In this example, I have searched for a file named "0444 Alignments" and found four instances on my hard drives. Only one of the files with this file name is located in the iPhoto Library.

While I cannot open the iPhoto Library if it is not working, I can open the 'Originals' folder. In it are a series of subfolders by year with all of my photos uploaded to iPhoto.

Once you have come to this point, you are on your own. Happy recovery!!

Wednesday, June 27, 2012

While making some chicken soup and thinking I should take a photograph, I looked around and saw there were other objects in the kitchen and nearby that did not look very photogenic. What if I could see in the infrared? What if I only saw shadows and highlights and nothing in between? Could I make these dull and mundane items look interesting and still retain enough of their original features to be recognizable?

My personal challenge was to photograph these otherwise dull, mundane, and not so photogenic objects then process the images in-camera with the Pentax K5 filters to bring out features otherwise hidden and see if they could be seen differently.

Glad wrap never seemed 'glad' to me, so what if it were brighter, the colors more saturated, and made the box stand out against a black background. Abstracted to unreality but definitely gladder looking!

You don't remember Easy Money? Well you are younger than I. Of course in real life we know there is no 'easy money' or 'free lunch' but there are those who would make us think so by claiming they can make us rich if only .... [fill in the blanks]. These charlatans must think their victims see things with a distorted view, so I used the distortion filter to make the Money look even easier. But you should take a 'clue' and see that there is a 'risk.'

Chinese Checkers! When I was six I thought this was the greatest game there was and probably because of the bright colors and the sound of marbles thumping distinctly against the metal surface of the board. That sound made it unlike any other board game. That sound is still brought to mind when I look at the distinctive appearance of this Chinese Checkers board.

This is what the coffee pot looks like in the morning before consuming the first cup!

Not in the kitchen, but seen from the kitchen window, the view of the boulder has recently been slightly obscured by an Elderberry limb that fell in last winter's storm. The boulder has a blue cast and the limb is a collection of linear features. I processed one image to bring out the blue in the boulder and the other to bring out the Elderberry limb.

Oh yes, what started all of this - the Kitchen Sink Chicken Soup! I call it that because I toss in whatever veggies I can find in the kitchen, though I did not include the sink.

Friday, June 01, 2012

Did you know you can customize the background design of your Twitter
page? It’s easy, though not straightforward,
so I thought it would be worthwhile to scribble some notes so I wouldn’t forget
how to do it! If it helps you, great.

The custom part of the design
is the background. I selected a background that matches my business card, then added the logo from my card, and contact information for my Flickr, Facebook, et al
webpages. For you, if the background is an image
that you upload, you will need to build your image in Photoshop or Lightroom
first. (I will not review creating the image, but note that I started with a
Photoshop canvas 2048 wide and 1600 wide and a marker line at 300 pixels to
restrict the area for text.)

Start with the home page, as in the example of my home page
below. But there is a problem with my first design. After creating the background image and
uploading it, I neglected to include my LinkedIn page. To correct that omission I went to my profile page. Under the head-and-shoulders icon on the
right side (next to the quill in a box) there is a pull-down menu. Go there and …

select View my profile
page followed by Edit Your Profile.

Then select the Design
option.

Once in the design page under Customize your own choose an
image file from your hard drive and upload it for the background.

Thursday, May 03, 2012

Darwin postulated that natural selection was the driver of
the evolution of species diversity[1]. Elements of this model include 1) a
selective agent as a reproductive threshold, 2) genetic variability
(plasticity), and 3) a continuing process of genetic variation with each
succeeding population. In the Darwinian
model of evolution, natural selection provides a reproductive threshold. Individuals within a population whose genetics
allow them to leave more offspring when challenged by the threshold determine
the genetic direction of the population and the process leads to a “more fit”
population, where fitness is defined in terms of the population’s ability to
adapt to the threshold. If the selective
agent threshold is too high, the population may become extinct.

For example, if color variation within a population of moths
allows some moths to escape predator detection then predation pressure would
act as a selective agent that would result in a change in distribution and
abundance of color expression within the moth population. Of course, the difficulty of finding
camouflaged moths could act on the predator population as a selective agent
driving changes that improved their abilities to detect prey. Another example would be the effect of
climatic variation on a population of Pika.
If genetic variation within the Pika population was insufficient to
provide some individuals who could survive and reproduce in the presence of
shifting summer temperatures, the Pika population would become extinct.

Mathematically these elements can be modeled. I have written a program in Excel using a
Monte-Carlo process to generate a population of values that can evolve into
another population of values in a “natural selection” process. In the model, two numbers define a starting
population: 1) the population average (mean) which represents Darwinian
fitness, and 2) population relative standard deviation which represents genetic
plasticity. Once a threshold is set, the
program generates a new population of values using the mean and relative
standard deviation (RSD). The
Monte-Carlo equations generate a Normal distribution of values with mean = 0
and standard deviation = 1. These values
are converted using the population mean (fitness) and RSD (genetic plasticity). Population values that exceed the threshold
become the basis for the next population.
The process can be repeated with or without a new threshold.

There are several observations I have made from this
model. Some seem reasonable and others
are counter-intuitive. All are potential
candidates for testing, though some might be trivial and others
cost-prohibitive. I leave the testing
for others to pursue.

Figure 1 is an example of the program output. The distribution of values will be referred to as a population. Fitness is the average value of the population. Spread is the standard deviation. Plasticity is the relative
standard deviation (RSD). Adaptation is the percentage of a distribution that exceeds a threshold. Options allow for specifying an initial and
challenge threshold, where the challenge threshold is set at a level just below
extinction. The extinction threshold is one that exceeds the highest value in a population. Survivors refer to population values that exceed a threshold.

The blue curve is for the initial population, Generation 1. The vertical axis represents values of the population
and the horizontal axis is the count of values (i.e., there are 100 individuals
in the population). The values have
been sorted to provide a less complex visualization of the population
distributions. Individual values in Generation1
vary from a low of 159 to a high of 257 with a fitness mean of 203 and a
plasticity RSD of 10. Values of Generation 1
that exceed the initial threshold of 230 are used to create Generation 2. A challenge threshold (250) was used to
create Generation 4 from Generation 3 (not shown). The table above the plot summarizes the
population statistics for each succeeding generation.

Figure 1: Monte-Carlo Evolution
Program Output Example (Run 1)

The most obvious observation is that there is variability
around the mean fitness value. Generation
1 ranges from a minimum of 159 to a maximum of 257. Note that the relative range between maximum
and minimum values decreases with each succeeding generation. I will discuss this more. That there is variability within a population
is a trivial observation given that these numbers are generated using a random
Monte-Carlo process, but it is worth noting.

The second observation is that for a given set of initial
fitness, genetic plasticity, and selective threshold the final outcome is only
generally predictable. Each run of the
program will generate a slightly different outcome. Figure 2 shows the second run with the same
starting inputs for mean, plasticity, and threshold values as the first run
shown in Figure 1. The changes are small
with a change in mean fitness for Generation 1 from 203 to 204, 241 to 240 for Generation 2,
and 255 to 254 for Generation 4.

Again, because this is a Monte-Carlo process this is a
trivial observation. The driver of
genetic plasticity is genetic mutation, a constraint driven but random
processes. A biological population with a specified fitness level and genetic
plasticity would not respond exactly the same way to each instance of a natural
selection challenge even if it were the exactly the same repeated
challenge. If this conclusion is
generally valid, the implication is that smaller population size would lead to
lower adaptability.

The third observation is that for a given fitness and
plasticity, there is a maximum threshold.
Setting the threshold above the maximum causes the program to
crash. I refer to this as the extinction
threshold. Because it is a Monte-Carlo
process, there is variability in the extinction threshold. A setting that causes a population crash in
one run may not result in a crash in the next run. In fact, the challenge threshold for Generation 4
of 250 was initially set high and was expected to result in a program crash
(extinction). It did on the third run,
as seen in Figure 3. Generation 3 had no survivors to contribute to Generation
4.

Figure 3: Setting the threshold
above extinction

What happens if the genetic plasticity is increased? If the initial fitness is fixed, changing the
plasticity would be comparable to having two biological populations with the
same overall average fitness but with different ranges from maximum to minimum
fitness. Figure 4 shows what happens
when the plasticity for the first run is doubled from 10% to 30%. Figure 4 shows that the fitness for Generations
2 and 4 has increased relative to run 1 even though the thresholds have not
changed. Generation 4, for example, went
from a mean fitness of 256 to 285. The
increase for Generation 1 from 199 to 205 is within the run-to-run variability
and not significant though the increase of the population maximum for an
individual value from 243 to 334 is. Note
that it would not be expected for the fitness of Generation 1 to change with an
increase (or decrease) in plasticity as it was not challenged with a
threshold.

Figure 4: Increasing genetic
plasticity

At first it would appear that an increase in plasticity may
not be of value as it not only leads to an increase in the maximum values in
the population (243 to 334 for Generation 1), but also decreases in the minimum
values (from 153 to 104 for Generation1).
Figure 5 used the input conditions for Run 3 with an increase in the
Challenge Threshold from 250 to 280.

Figure 5: Increasing the Challenge Threshold after an
increase in plasticity but a fixed initial fitness

The Challenge Threshold is set to affect only Generation 4
and the results are consistent with that condition. The mean fitness of Generation 2 shows a
modest increase, but is within the run-to-run reproducibility. Generation 4, however, shows a much larger
increase from 285 to 300 and both the max and min values have increased. The significance for a biological population
is that even if the population has some individuals that are well below the
mean fitness, the population as a whole may be more responsive to environmental
challenges.

In this model of evolution, is genetic plasticity or genetic
fitness a better predictor of adaptability?
I have rewritten the model to allow for multiple runs with a single set
of input values for fitness, plasticity, and threshold. The percentage of values exceeding a
threshold (PECT) is a measure of adaptability.

The model was run twenty times for each set of plasticity
and fitness. Column 1 is the Run number,
column 2 is population 1 with fitness of 220 and a plasticity of 10, and column
4 is population 2 with fitness of 190 and a plasticity of 40. The data are presented in Table 2 (note that
there is no Table 1). In this example,
population 2 with the higher plasticity has the higher PECT (57.8 vs 11).

This is somewhat counter-intuitive. Consider if the players in a football team at
school A were very evenly matched and all could bench press 200 plus or minus
10 pounds while players at school B could press 190 plus or minus 40
pounds. You might think that the team
with the well matched players and the higher average bench press capabilities
would be more capable of achieving a 250 pound bench press challenge. But in fact only 11% of team A could do that
while 58% of team B could meet that challenge.

This mathematical model of evolution is a simple one and
does not capture the complexity of biological evolution. For example, predator-prey interactions,
group selection, and eco-system feedback loops are not part of this model. Given its simplicity, however, it has
provided a surprising number of testable hypotheses:

Increased
genetic plasticity increases adaptability.

There
are limits to adaptability for a given plasticity and genetic fitness: a
selection threshold that is too high will result in extinction.

In an environment where the threshold is changing, adaptation to a challenge
threshold is greater for the population that has been subjected to the higher non-extinction threshold below the challenge threshold.

As
the selection threshold increases, genetic plasticity of a population
decreases.

Genetic
plasticity can be a better predictor of adaptability than population fitness.

If you would like a copy of the 'Evolution' program, please write me. You may use the program for your own purposes or post on your blog with credit. You may not use the program for commercial purposes. Write me at kozborn@sbcglobal.net.

[1]Charles Darwin, "On the Origin of Species by Means of Natural
Selection, or the Preservation of Favoured Races in the Struggle for
Life," 1859.

Wednesday, April 04, 2012

L.A.B. is a color profile like RGB. Unlike RGB, L.A.B. separates colors into two
channels, the ‘a’ and the ‘b’, and the luminosity into a third, ‘L’,
channel. Thus working with L.A.B.,
unlike working in RGB, allows adjustments to the color independently of the
luminosity.

The default color mode in Photoshop is usually RGB. To convert an image from RGB to L.A.B. use
either the Image>Mode>Lab Color (Fig 1) or Edit>Convert to Profile
(Fig 2). Dan Margulis (“Photoshop Lab
Color”) recommends converting to profile.

Fig 1: Changing color mode

Fig 2: Convert to Profile

Before showing how to use L.A.B. on an image, I’ll give a
tweek or two using RGB so you can compare the results. I’ll use an image that I think has some
potential but is low in contrast and a bit soft (Fig 3). A quick Auto Adjust in Levels gives the image
a bit of snap (Fig 4), but maybe it could be improved a bit more.

Fig 3: Two Tigers at the Oakland Zoo - a bit flat and could be a little sharper

Fig 4: Auto Adjust in RGB Levels - looks better

So let’s see what L.A.B. can do. Fig 5 shows the same image as Fig 1 after
conversion to L.A.B. There is no obvious
visual difference between the two images.

The histograms are different. The RGB histogram (fig 6, on right) shows the
pixel counts for pixel values 0-255 for each of the three color channels. L.A.B. separates the two color channels (a
and b) from the lightness channel (bottom of the L.A.B. histogram on left). While both graphic histograms use a plot of
counts vs levels (aka pixel values), the values in L.A.B. are specified
somewhat differently (Fig 7).

RGB has values of [128, 128, 128] for middle gray. L.A.B. has color values of [0,0] for middle
gray and 54 for lightness. If the values for a and b are
fixed at [0,0] any changes in the L channel will not change the neutrality of
the color. It can be changed from pure
white to pure black and every tone of gray in between but no changes to the L
channel will introduce any color.

Let’s take another look at the tiger image to see how this
works in practice. On the left side in Fig 8
the channel a curves slider has been moved to the left resulting in a magenta
cast. On the right side the slider has been moved to the right giving a
green cast. I call the a channel the
apple-green-magenta channel. Don’t know
if that will work for you, but if you look at Fig 9, you might guess what I’ll
use for the b channel!

Fig 8: Moving the channel a curves slider

Fig 9: Moving the channel b curves slider

Moving the b slider to the right adds blue and moving to the
left adds yellow. I call the b channel
the banana-yellow-blue channel. You saw
that coming, right? No? Doesn't matter but you might just remember it unless you eat magenta apples and blue bananas.

Let’s combine slider movements so they are symmetrically
equal as in Fig 10. Using channel b, the
tigers show a little more life with an equal boost to both the yellow and blue
components. That looks a little more
realistic. If the green and magenta are
also increased in channel a, it gets even better.

Fig 10: equal additions of blue and yellow in channel b
followed by equal additions of magenta and green in channel a

So far I’ve just changed the color but not the lightness
value. The image is still a bit flat,
lacking contrast. When the Lightness channel is adjusted, that changes as in
Fig 11. The tiger now has some life!

Fig 11: Bringing the tiger to life with the L channel

I could probably stop here, but I won’t because there is a
sharpening trick that works really nicely in L.A.B. Because the color channels are separate from
the lightness channel, sharpening does not affect the colors. I will combine this process with another for
one last attempt to give life to the tiger.

Duplicate the original layer and merge in blend mode
multiply. Egad! This is not an improvement! Stay with me.

Fig 12: Tigers thrown into the dark

Now add a mask and Image>Apply Image as in Fig 13. Notice that the mask for the upper layer now
has some black inside the frame. This is
a selection that we can adjust to change both the luminosity and sharpness. Make sure the mask is selected before using Apply Image or adjusting the Feather slider in the following two steps.

Fig 13: Using Apply Image

Make sure the ‘mask’ window is viewable (if not Window>Masks)
and move the feather slider to the right.
You should see a noticeable increase in sharpness as in Fig 14. Notice the Density slider. If this slider is
moved to the left, the effect of the mask is reduced and the image will
approach its original appearance. So if
you want it a little bit darker, go for it.
I’m leaving it where it is now.

Fig 14: Feather slider in Mask window to increase sharpness.

Fig 15: The tigers can now play

References:
Margulis, Dan "Photoshop LAB Color - The Canyon Conundrum and Other Adventures in the Most Powerful Colorspace," 2006, Peachpit Press, Berkeley.
Mark Lindsay, M.F.A. and a presentation to the Berkeley Camera Club on March 28, 2012
see Mark's work at marklindsayart.com